Skip to content

Instantly share code, notes, and snippets.

@astha-29

astha-29/mini-project.ipynb

Created August 13, 2020 07:01
Show Gist options
  • Save astha-29/583db6f6b9eff73f8a1f14eab7446b8b to your computer and use it in GitHub Desktop.
Save astha-29/583db6f6b9eff73f8a1f14eab7446b8b to your computer and use it in GitHub Desktop.
Download ZIP
Mini Project.ipynb
Display the source blob
Display the rendered blob
Raw
mini-project.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Mini Project.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyNSrbQeYNhL9zoIWVTh18r6",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/astha-29/583db6f6b9eff73f8a1f14eab7446b8b/mini-project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WD3KV2nNlIBa",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ALeEBumTlgUN",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 415
},
"outputId": "93abcff1-7058-4275-9f75-b8fc5421dda6"
},
"source": [
"df= pd.read_csv('advertising.csv')\n",
"df"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>16.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>17.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>38.2</td>\n",
" <td>3.7</td>\n",
" <td>13.8</td>\n",
" <td>7.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>94.2</td>\n",
" <td>4.9</td>\n",
" <td>8.1</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>177.0</td>\n",
" <td>9.3</td>\n",
" <td>6.4</td>\n",
" <td>14.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>283.6</td>\n",
" <td>42.0</td>\n",
" <td>66.2</td>\n",
" <td>25.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>232.1</td>\n",
" <td>8.6</td>\n",
" <td>8.7</td>\n",
" <td>18.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>200 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales\n",
"0 230.1 37.8 69.2 22.1\n",
"1 44.5 39.3 45.1 10.4\n",
"2 17.2 45.9 69.3 12.0\n",
"3 151.5 41.3 58.5 16.5\n",
"4 180.8 10.8 58.4 17.9\n",
".. ... ... ... ...\n",
"195 38.2 3.7 13.8 7.6\n",
"196 94.2 4.9 8.1 14.0\n",
"197 177.0 9.3 6.4 14.8\n",
"198 283.6 42.0 66.2 25.5\n",
"199 232.1 8.6 8.7 18.4\n",
"\n",
"[200 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "d1QwkFh-lqTM",
"colab_type": "code",
"colab": {}
},
"source": [
"x=df.iloc[:,0:3].values\n",
"y=df.iloc[:,3].values"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YiDH0SD1lwwd",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3,random_state =3)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JABqG_Fsl0xV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "1f8161f0-2f60-48e0-ef8f-483cdce84a4f"
},
"source": [
"x_train.shape"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(140, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cNC2u1IVl3cj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "134b422d-6226-4db5-f5f5-be5b39c0e307"
},
"source": [
"x_test.shape"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(60, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XVCTJ9gol5r6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 225
},
"outputId": "1fd6d8ac-f2fc-4da3-f8e7-192625cfb5ff"
},
"source": [
"from sklearn.linear_model import LinearRegression\n",
"model = LinearRegression()\n",
"model.fit(x_train,y_train)\n",
"y_pred = model.predict(x_test)\n",
"y_pred"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([17.94221632, 11.28731032, 19.36406753, 15.25309499, 8.85035488,\n",
" 11.08345095, 24.54827272, 10.72184726, 18.64190205, 17.03877174,\n",
" 14.71887065, 13.30204368, 19.10529921, 11.4654086 , 13.82417942,\n",
" 14.56139355, 16.86156735, 17.27369971, 17.78634747, 21.28201581,\n",
" 19.1397699 , 11.05346066, 9.93276334, 11.49854807, 8.5309559 ,\n",
" 13.26073545, 21.75566382, 16.96066432, 24.25791572, 11.92392893,\n",
" 16.40376866, 21.96064207, 9.51770237, 10.16209996, 10.08141197,\n",
" 10.45644324, 15.54919097, 9.92133897, 13.83425453, 12.54320065,\n",
" 14.5093965 , 12.61758414, 6.46804914, 20.25656292, 23.16303373,\n",
" 24.65508581, 15.20817964, 9.27513655, 18.72004324, 18.16217728,\n",
" 12.73063894, 16.65175796, 15.79776032, 8.36188762, 21.22771856,\n",
" 9.52094834, 23.88078008, 23.29062902, 19.6930198 , 16.76467522])"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nmMsa5GLl7ga",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121
},
"outputId": "beb88f11-84f2-468e-f3a6-f3cafe0f0b69"
},
"source": [
"y_test"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([16.6, 10.7, 20.7, 14.8, 8.4, 11.3, 26.2, 10.6, 18.3, 16.7, 17.6,\n",
" 13.6, 17. , 10.8, 12.9, 16.7, 16.9, 16.5, 17.1, 23.8, 19. , 10.4,\n",
" 9.2, 11.8, 8.7, 13.3, 24.7, 16.6, 23.8, 12. , 16. , 23.2, 7.3,\n",
" 11.9, 10.1, 14. , 17.9, 8.8, 13.4, 10.3, 15.3, 12.6, 3.2, 22.4,\n",
" 24.2, 27. , 15.5, 1.6, 20.5, 16. , 11.9, 19.7, 16.7, 5.7, 19.8,\n",
" 6.6, 24.4, 21.4, 16.8, 19.9])"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tpTSH1jBl-Rf",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "3698026c-11af-40c7-af9e-2bc837d7076a"
},
"source": [
"from sklearn import metrics\n",
"metrics.r2_score(y_test,y_pred)*100 #Accuracy"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"88.77675297095176"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gblu5_8emAnH",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment